Overview

Dataset statistics

Number of variables21
Number of observations5358
Missing cells241
Missing cells (%)0.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.3 MiB
Average record size in memory452.2 B

Variable types

Numeric13
Boolean1
Categorical7

Alerts

heart_disease is highly imbalanced (67.1%)Imbalance
hypertension is highly imbalanced (51.5%)Imbalance
stroke is highly imbalanced (55.4%)Imbalance
bmi has 241 (4.5%) missing valuesMissing
feat01 has unique valuesUnique
feat02 has unique valuesUnique
feat03 has unique valuesUnique
feat04 has unique valuesUnique
feat05 has unique valuesUnique
feat06 has unique valuesUnique
feat07 has unique valuesUnique
feat08 has unique valuesUnique
feat09 has unique valuesUnique
feat10 has unique valuesUnique

Reproduction

Analysis started2024-03-07 22:26:37.770712
Analysis finished2024-03-07 22:26:49.152844
Duration11.38 seconds
Software versionydata-profiling vv4.6.4
Download configurationconfig.json

Variables

age
Real number (ℝ)

Distinct104
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44.368481
Minimum0.08
Maximum82
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size83.7 KiB
2024-03-07T23:26:49.208374image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Quantile statistics

Minimum0.08
5-th percentile5
Q126
median46
Q362
95-th percentile79
Maximum82
Range81.92
Interquartile range (IQR)36

Descriptive statistics

Standard deviation22.84083
Coefficient of variation (CV)0.51479855
Kurtosis-0.99640242
Mean44.368481
Median Absolute Deviation (MAD)18
Skewness-0.18401757
Sum237726.32
Variance521.7035
MonotonicityNot monotonic
2024-03-07T23:26:49.295319image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
78 123
 
2.3%
57 106
 
2.0%
79 102
 
1.9%
54 93
 
1.7%
52 92
 
1.7%
51 90
 
1.7%
59 88
 
1.6%
45 88
 
1.6%
80 87
 
1.6%
53 87
 
1.6%
Other values (94) 4402
82.2%
ValueCountFrequency (%)
0.08 2
 
< 0.1%
0.16 3
0.1%
0.24 5
0.1%
0.32 5
0.1%
0.4 2
 
< 0.1%
0.48 3
0.1%
0.56 5
0.1%
0.64 4
0.1%
0.72 5
0.1%
0.8 4
0.1%
ValueCountFrequency (%)
82 65
1.2%
81 74
1.4%
80 87
1.6%
79 102
1.9%
78 123
2.3%
77 50
0.9%
76 60
1.1%
75 59
1.1%
74 49
 
0.9%
73 50
0.9%

avg_glucose_level
Real number (ℝ)

Distinct4167
Distinct (%)77.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean107.37692
Minimum48.78
Maximum271.74
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size83.7 KiB
2024-03-07T23:26:49.387797image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Quantile statistics

Minimum48.78
5-th percentile60.7255
Q177.335
median92.225
Q3115.39
95-th percentile217.7975
Maximum271.74
Range222.96
Interquartile range (IQR)38.055

Descriptive statistics

Standard deviation46.576141
Coefficient of variation (CV)0.43376306
Kurtosis1.406092
Mean107.37692
Median Absolute Deviation (MAD)17.98
Skewness1.5128362
Sum575325.54
Variance2169.3369
MonotonicityNot monotonic
2024-03-07T23:26:49.472009image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
93.88 6
 
0.1%
73 5
 
0.1%
91.85 5
 
0.1%
78.24 5
 
0.1%
84.1 5
 
0.1%
91.68 5
 
0.1%
72.49 5
 
0.1%
83.16 5
 
0.1%
90.42 4
 
0.1%
114.32 4
 
0.1%
Other values (4157) 5309
99.1%
ValueCountFrequency (%)
48.78 1
< 0.1%
49.23 1
< 0.1%
52.06 1
< 0.1%
52.54 1
< 0.1%
55.12 1
< 0.1%
55.22 1
< 0.1%
55.23 1
< 0.1%
55.25 1
< 0.1%
55.26 1
< 0.1%
55.27 1
< 0.1%
ValueCountFrequency (%)
271.74 1
< 0.1%
269.12 1
< 0.1%
268.02 1
< 0.1%
267.76 1
< 0.1%
267.61 1
< 0.1%
267.6 1
< 0.1%
266.59 1
< 0.1%
263.56 1
< 0.1%
263.32 1
< 0.1%
261.67 1
< 0.1%

bmi
Real number (ℝ)

MISSING 

Distinct418
Distinct (%)8.2%
Missing241
Missing (%)4.5%
Infinite0
Infinite (%)0.0%
Mean28.95896
Minimum10.3
Maximum97.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size83.7 KiB
2024-03-07T23:26:49.542481image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Quantile statistics

Minimum10.3
5-th percentile17.7
Q123.7
median28.1
Q333.1
95-th percentile42.92
Maximum97.6
Range87.3
Interquartile range (IQR)9.4

Descriptive statistics

Standard deviation7.8036118
Coefficient of variation (CV)0.26947141
Kurtosis3.3229195
Mean28.95896
Median Absolute Deviation (MAD)4.7
Skewness1.0427852
Sum148183
Variance60.896357
MonotonicityNot monotonic
2024-03-07T23:26:49.625787image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
28.4 41
 
0.8%
28.7 41
 
0.8%
27.3 41
 
0.8%
27.7 40
 
0.7%
26.1 39
 
0.7%
26.7 38
 
0.7%
27.6 37
 
0.7%
23.4 37
 
0.7%
26.4 37
 
0.7%
27 37
 
0.7%
Other values (408) 4729
88.3%
(Missing) 241
 
4.5%
ValueCountFrequency (%)
10.3 1
< 0.1%
11.3 1
< 0.1%
11.5 1
< 0.1%
12 1
< 0.1%
12.3 1
< 0.1%
12.8 1
< 0.1%
13 1
< 0.1%
13.2 1
< 0.1%
13.3 1
< 0.1%
13.4 1
< 0.1%
ValueCountFrequency (%)
97.6 1
< 0.1%
92 1
< 0.1%
78 1
< 0.1%
71.9 1
< 0.1%
66.8 1
< 0.1%
64.8 1
< 0.1%
64.4 1
< 0.1%
63.3 1
< 0.1%
61.6 1
< 0.1%
61.2 1
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size47.1 KiB
True
3573 
False
1785 
ValueCountFrequency (%)
True 3573
66.7%
False 1785
33.3%
2024-03-07T23:26:49.688304image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

feat01
Real number (ℝ)

UNIQUE 

Distinct5358
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.52513684
Minimum0
Maximum1
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size83.7 KiB
2024-03-07T23:26:49.759803image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.30794303
Q10.4335678
median0.5259077
Q30.61569946
95-th percentile0.74025097
Maximum1
Range1
Interquartile range (IQR)0.18213166

Descriptive statistics

Standard deviation0.13312093
Coefficient of variation (CV)0.25349761
Kurtosis0.01484692
Mean0.52513684
Median Absolute Deviation (MAD)0.09107641
Skewness0.006166905
Sum2813.6832
Variance0.017721183
MonotonicityNot monotonic
2024-03-07T23:26:49.848717image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.621595254 1
 
< 0.1%
0.6183524476 1
 
< 0.1%
0.6691209102 1
 
< 0.1%
0.1979663495 1
 
< 0.1%
0.6525026389 1
 
< 0.1%
0.598753879 1
 
< 0.1%
0.4919099083 1
 
< 0.1%
0.6139811207 1
 
< 0.1%
0.3173576375 1
 
< 0.1%
0.7485217641 1
 
< 0.1%
Other values (5348) 5348
99.8%
ValueCountFrequency (%)
0 1
< 0.1%
0.05388364358 1
< 0.1%
0.09303162613 1
< 0.1%
0.1040040418 1
< 0.1%
0.1163754095 1
< 0.1%
0.1211733956 1
< 0.1%
0.1224188506 1
< 0.1%
0.1257523209 1
< 0.1%
0.1366062527 1
< 0.1%
0.1407255018 1
< 0.1%
ValueCountFrequency (%)
1 1
< 0.1%
0.984181102 1
< 0.1%
0.9709808517 1
< 0.1%
0.9553925935 1
< 0.1%
0.9398492649 1
< 0.1%
0.9334781765 1
< 0.1%
0.9322174586 1
< 0.1%
0.9308279064 1
< 0.1%
0.9267348392 1
< 0.1%
0.9209119074 1
< 0.1%

feat02
Real number (ℝ)

UNIQUE 

Distinct5358
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.54666718
Minimum0
Maximum1
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size83.7 KiB
2024-03-07T23:26:49.934724image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.368617
Q10.47374563
median0.54805112
Q30.62005467
95-th percentile0.72188342
Maximum1
Range1
Interquartile range (IQR)0.14630904

Descriptive statistics

Standard deviation0.10947394
Coefficient of variation (CV)0.20025703
Kurtosis0.40311174
Mean0.54666718
Median Absolute Deviation (MAD)0.073399272
Skewness-0.09831506
Sum2929.0428
Variance0.011984544
MonotonicityNot monotonic
2024-03-07T23:26:50.021089image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.6635929648 1
 
< 0.1%
0.66598444 1
 
< 0.1%
0.5852383768 1
 
< 0.1%
0.5990650715 1
 
< 0.1%
0.4675276872 1
 
< 0.1%
0.4155361275 1
 
< 0.1%
0.5462785316 1
 
< 0.1%
0.4295662394 1
 
< 0.1%
0.5192628732 1
 
< 0.1%
0.2856893063 1
 
< 0.1%
Other values (5348) 5348
99.8%
ValueCountFrequency (%)
0 1
< 0.1%
0.1310315863 1
< 0.1%
0.1364974554 1
< 0.1%
0.1444300723 1
< 0.1%
0.1566091301 1
< 0.1%
0.1574059949 1
< 0.1%
0.1609613856 1
< 0.1%
0.1614601483 1
< 0.1%
0.1624248336 1
< 0.1%
0.1646659644 1
< 0.1%
ValueCountFrequency (%)
1 1
< 0.1%
0.9961262852 1
< 0.1%
0.9342439475 1
< 0.1%
0.9283553296 1
< 0.1%
0.9164246844 1
< 0.1%
0.9102583301 1
< 0.1%
0.8956797273 1
< 0.1%
0.8951763522 1
< 0.1%
0.8929880803 1
< 0.1%
0.891254081 1
< 0.1%

feat03
Real number (ℝ)

UNIQUE 

Distinct5358
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.99545631
Minimum0.14130948
Maximum1.825912
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size83.7 KiB
2024-03-07T23:26:50.101414image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Quantile statistics

Minimum0.14130948
5-th percentile0.48472633
Q10.73702547
median0.99734957
Q31.2524597
95-th percentile1.4991048
Maximum1.825912
Range1.6846025
Interquartile range (IQR)0.51543422

Descriptive statistics

Standard deviation0.32016912
Coefficient of variation (CV)0.32163051
Kurtosis-0.86006065
Mean0.99545631
Median Absolute Deviation (MAD)0.2573103
Skewness-0.023603625
Sum5333.6549
Variance0.10250827
MonotonicityNot monotonic
2024-03-07T23:26:50.184074image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.070287519 1
 
< 0.1%
1.424324095 1
 
< 0.1%
0.4927195851 1
 
< 0.1%
0.8661140849 1
 
< 0.1%
1.205735232 1
 
< 0.1%
1.34448327 1
 
< 0.1%
0.9307655188 1
 
< 0.1%
0.7118831876 1
 
< 0.1%
0.8222009078 1
 
< 0.1%
0.6917179792 1
 
< 0.1%
Other values (5348) 5348
99.8%
ValueCountFrequency (%)
0.141309484 1
< 0.1%
0.1863412145 1
< 0.1%
0.187432667 1
< 0.1%
0.1902409092 1
< 0.1%
0.1958973176 1
< 0.1%
0.2094063171 1
< 0.1%
0.2124708912 1
< 0.1%
0.2195362786 1
< 0.1%
0.2344893745 1
< 0.1%
0.2473023577 1
< 0.1%
ValueCountFrequency (%)
1.825911995 1
< 0.1%
1.785165513 1
< 0.1%
1.782425796 1
< 0.1%
1.770704951 1
< 0.1%
1.754184163 1
< 0.1%
1.75034002 1
< 0.1%
1.749165374 1
< 0.1%
1.746170831 1
< 0.1%
1.740651478 1
< 0.1%
1.730824923 1
< 0.1%

feat04
Real number (ℝ)

UNIQUE 

Distinct5358
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.97687495
Minimum0.059627079
Maximum1.8602682
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size83.7 KiB
2024-03-07T23:26:50.266387image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Quantile statistics

Minimum0.059627079
5-th percentile0.46165402
Q10.72647465
median0.98002301
Q31.2290659
95-th percentile1.4900795
Maximum1.8602682
Range1.8006411
Interquartile range (IQR)0.50259122

Descriptive statistics

Standard deviation0.32102003
Coefficient of variation (CV)0.32861937
Kurtosis-0.74278901
Mean0.97687495
Median Absolute Deviation (MAD)0.25157407
Skewness-0.0053176521
Sum5234.096
Variance0.10305386
MonotonicityNot monotonic
2024-03-07T23:26:50.341771image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.274482792 1
 
< 0.1%
0.2864485666 1
 
< 0.1%
0.4963146855 1
 
< 0.1%
1.055589954 1
 
< 0.1%
0.6567110023 1
 
< 0.1%
1.393063741 1
 
< 0.1%
0.7870504142 1
 
< 0.1%
0.9017540625 1
 
< 0.1%
0.6782619824 1
 
< 0.1%
1.336831757 1
 
< 0.1%
Other values (5348) 5348
99.8%
ValueCountFrequency (%)
0.0596270794 1
< 0.1%
0.09346263189 1
< 0.1%
0.1150778179 1
< 0.1%
0.1171352699 1
< 0.1%
0.1485754403 1
< 0.1%
0.1505095666 1
< 0.1%
0.1623359498 1
< 0.1%
0.1716458787 1
< 0.1%
0.1742476374 1
< 0.1%
0.1751816759 1
< 0.1%
ValueCountFrequency (%)
1.860268227 1
< 0.1%
1.840626215 1
< 0.1%
1.792419791 1
< 0.1%
1.778735049 1
< 0.1%
1.771101038 1
< 0.1%
1.759572625 1
< 0.1%
1.750964809 1
< 0.1%
1.749793315 1
< 0.1%
1.747565401 1
< 0.1%
1.730824657 1
< 0.1%

feat05
Real number (ℝ)

UNIQUE 

Distinct5358
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0425825
Minimum0.21886148
Maximum1.8057262
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size83.7 KiB
2024-03-07T23:26:50.411501image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Quantile statistics

Minimum0.21886148
5-th percentile0.54433344
Q10.79979905
median1.0427535
Q31.2960868
95-th percentile1.5403157
Maximum1.8057262
Range1.5868648
Interquartile range (IQR)0.49628771

Descriptive statistics

Standard deviation0.31367227
Coefficient of variation (CV)0.30086086
Kurtosis-0.84096529
Mean1.0425825
Median Absolute Deviation (MAD)0.24736249
Skewness-0.020832173
Sum5586.1571
Variance0.098390291
MonotonicityNot monotonic
2024-03-07T23:26:50.485835image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.39072629 1
 
< 0.1%
0.9691956188 1
 
< 0.1%
0.7056552744 1
 
< 0.1%
1.11328646 1
 
< 0.1%
0.4848288494 1
 
< 0.1%
1.127301673 1
 
< 0.1%
1.014182622 1
 
< 0.1%
1.198664276 1
 
< 0.1%
0.8169382182 1
 
< 0.1%
0.8557240435 1
 
< 0.1%
Other values (5348) 5348
99.8%
ValueCountFrequency (%)
0.2188614787 1
< 0.1%
0.2367464905 1
< 0.1%
0.2379913873 1
< 0.1%
0.2472722143 1
< 0.1%
0.249595708 1
< 0.1%
0.2542457215 1
< 0.1%
0.2853861654 1
< 0.1%
0.2898982673 1
< 0.1%
0.2945679715 1
< 0.1%
0.2968461633 1
< 0.1%
ValueCountFrequency (%)
1.80572623 1
< 0.1%
1.796663848 1
< 0.1%
1.783563918 1
< 0.1%
1.7742431 1
< 0.1%
1.76792078 1
< 0.1%
1.767622679 1
< 0.1%
1.762110799 1
< 0.1%
1.740363849 1
< 0.1%
1.737065846 1
< 0.1%
1.735609315 1
< 0.1%

feat06
Real number (ℝ)

UNIQUE 

Distinct5358
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.960986
Minimum0.020592485
Maximum1.8525569
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size83.7 KiB
2024-03-07T23:26:50.559543image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Quantile statistics

Minimum0.020592485
5-th percentile0.4562238
Q10.71309477
median0.95703246
Q31.2127147
95-th percentile1.4719313
Maximum1.8525569
Range1.8319644
Interquartile range (IQR)0.49961993

Descriptive statistics

Standard deviation0.31903023
Coefficient of variation (CV)0.33198219
Kurtosis-0.78984216
Mean0.960986
Median Absolute Deviation (MAD)0.25026804
Skewness0.013897199
Sum5148.963
Variance0.10178029
MonotonicityNot monotonic
2024-03-07T23:26:50.633900image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.096495121 1
 
< 0.1%
0.8331488576 1
 
< 0.1%
1.244903861 1
 
< 0.1%
0.8603875094 1
 
< 0.1%
0.6479717837 1
 
< 0.1%
0.6991440993 1
 
< 0.1%
0.6784443601 1
 
< 0.1%
1.502761255 1
 
< 0.1%
1.378929793 1
 
< 0.1%
1.126531658 1
 
< 0.1%
Other values (5348) 5348
99.8%
ValueCountFrequency (%)
0.02059248516 1
< 0.1%
0.1301524523 1
< 0.1%
0.1402795358 1
< 0.1%
0.1640919546 1
< 0.1%
0.169239146 1
< 0.1%
0.1714024248 1
< 0.1%
0.1862395334 1
< 0.1%
0.1874437828 1
< 0.1%
0.2015040596 1
< 0.1%
0.2136665299 1
< 0.1%
ValueCountFrequency (%)
1.852556901 1
< 0.1%
1.793459164 1
< 0.1%
1.772204372 1
< 0.1%
1.757051342 1
< 0.1%
1.751196453 1
< 0.1%
1.731595263 1
< 0.1%
1.730526218 1
< 0.1%
1.729119764 1
< 0.1%
1.727033288 1
< 0.1%
1.720177288 1
< 0.1%

feat07
Real number (ℝ)

UNIQUE 

Distinct5358
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.97842701
Minimum0.10563417
Maximum1.8549244
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size83.7 KiB
2024-03-07T23:26:50.704697image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Quantile statistics

Minimum0.10563417
5-th percentile0.46041422
Q10.72329313
median0.97859031
Q31.2294206
95-th percentile1.4960207
Maximum1.8549244
Range1.7492902
Interquartile range (IQR)0.50612748

Descriptive statistics

Standard deviation0.32446318
Coefficient of variation (CV)0.33161716
Kurtosis-0.77657653
Mean0.97842701
Median Absolute Deviation (MAD)0.25338521
Skewness0.0020678352
Sum5242.4119
Variance0.10527636
MonotonicityNot monotonic
2024-03-07T23:26:50.774076image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.071132479 1
 
< 0.1%
1.060315247 1
 
< 0.1%
0.5097454639 1
 
< 0.1%
0.5706227177 1
 
< 0.1%
1.133088619 1
 
< 0.1%
1.377433587 1
 
< 0.1%
1.27569164 1
 
< 0.1%
1.195355945 1
 
< 0.1%
0.848425418 1
 
< 0.1%
1.265524139 1
 
< 0.1%
Other values (5348) 5348
99.8%
ValueCountFrequency (%)
0.1056341688 1
< 0.1%
0.1355642203 1
< 0.1%
0.1363316521 1
< 0.1%
0.1509909762 1
< 0.1%
0.1694056725 1
< 0.1%
0.1815204809 1
< 0.1%
0.2038740268 1
< 0.1%
0.2071057508 1
< 0.1%
0.2098381893 1
< 0.1%
0.2131277017 1
< 0.1%
ValueCountFrequency (%)
1.854924375 1
< 0.1%
1.798845367 1
< 0.1%
1.797420585 1
< 0.1%
1.789366331 1
< 0.1%
1.78809527 1
< 0.1%
1.751375977 1
< 0.1%
1.743423032 1
< 0.1%
1.738613667 1
< 0.1%
1.731347045 1
< 0.1%
1.725448114 1
< 0.1%

feat08
Real number (ℝ)

UNIQUE 

Distinct5358
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.99493927
Minimum0.13911547
Maximum1.8986577
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size83.7 KiB
2024-03-07T23:26:50.842736image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Quantile statistics

Minimum0.13911547
5-th percentile0.49299652
Q10.74173137
median0.99089155
Q31.24932
95-th percentile1.5031658
Maximum1.8986577
Range1.7595422
Interquartile range (IQR)0.50758861

Descriptive statistics

Standard deviation0.31836363
Coefficient of variation (CV)0.31998298
Kurtosis-0.78332711
Mean0.99493927
Median Absolute Deviation (MAD)0.25426443
Skewness0.027175865
Sum5330.8846
Variance0.1013554
MonotonicityNot monotonic
2024-03-07T23:26:50.913327image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.174714983 1
 
< 0.1%
0.75132993 1
 
< 0.1%
1.202632119 1
 
< 0.1%
1.314971234 1
 
< 0.1%
0.5067394455 1
 
< 0.1%
0.8743426885 1
 
< 0.1%
1.232688854 1
 
< 0.1%
0.4975841887 1
 
< 0.1%
0.9368459147 1
 
< 0.1%
1.508995487 1
 
< 0.1%
Other values (5348) 5348
99.8%
ValueCountFrequency (%)
0.1391154654 1
< 0.1%
0.1773982665 1
< 0.1%
0.181149346 1
< 0.1%
0.1982485874 1
< 0.1%
0.207358003 1
< 0.1%
0.2115219569 1
< 0.1%
0.2303711317 1
< 0.1%
0.2377981569 1
< 0.1%
0.2401691765 1
< 0.1%
0.2464290369 1
< 0.1%
ValueCountFrequency (%)
1.898657672 1
< 0.1%
1.881526673 1
< 0.1%
1.853972434 1
< 0.1%
1.850176218 1
< 0.1%
1.841998523 1
< 0.1%
1.815863708 1
< 0.1%
1.814892197 1
< 0.1%
1.801268908 1
< 0.1%
1.798640798 1
< 0.1%
1.795521369 1
< 0.1%

feat09
Real number (ℝ)

UNIQUE 

Distinct5358
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0225358
Minimum0.22015082
Maximum1.8351232
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size83.7 KiB
2024-03-07T23:26:50.982739image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Quantile statistics

Minimum0.22015082
5-th percentile0.51541512
Q10.77055066
median1.0235428
Q31.270791
95-th percentile1.5279652
Maximum1.8351232
Range1.6149723
Interquartile range (IQR)0.50024037

Descriptive statistics

Standard deviation0.31670844
Coefficient of variation (CV)0.30972848
Kurtosis-0.83831311
Mean1.0225358
Median Absolute Deviation (MAD)0.25024441
Skewness-0.010658559
Sum5478.7466
Variance0.10030424
MonotonicityNot monotonic
2024-03-07T23:26:51.056569image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.13074268 1
 
< 0.1%
0.8225552307 1
 
< 0.1%
0.8041851051 1
 
< 0.1%
0.8385903028 1
 
< 0.1%
1.303899949 1
 
< 0.1%
0.8547019059 1
 
< 0.1%
1.067979556 1
 
< 0.1%
0.9511797321 1
 
< 0.1%
0.7746973688 1
 
< 0.1%
0.7328872253 1
 
< 0.1%
Other values (5348) 5348
99.8%
ValueCountFrequency (%)
0.2201508232 1
< 0.1%
0.2407530786 1
< 0.1%
0.244268671 1
< 0.1%
0.2445341141 1
< 0.1%
0.251263091 1
< 0.1%
0.2555038524 1
< 0.1%
0.2669598588 1
< 0.1%
0.2677980381 1
< 0.1%
0.2817727676 1
< 0.1%
0.2842435725 1
< 0.1%
ValueCountFrequency (%)
1.835123154 1
< 0.1%
1.815400124 1
< 0.1%
1.800039598 1
< 0.1%
1.776735975 1
< 0.1%
1.772870674 1
< 0.1%
1.767963665 1
< 0.1%
1.763823876 1
< 0.1%
1.759082281 1
< 0.1%
1.758535892 1
< 0.1%
1.752460018 1
< 0.1%

feat10
Real number (ℝ)

UNIQUE 

Distinct5358
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.51703219
Minimum0
Maximum1
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size83.7 KiB
2024-03-07T23:26:51.132179image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.28798434
Q10.42597484
median0.51842631
Q30.61000856
95-th percentile0.74493443
Maximum1
Range1
Interquartile range (IQR)0.18403372

Descriptive statistics

Standard deviation0.13826758
Coefficient of variation (CV)0.26742547
Kurtosis-0.043330428
Mean0.51703219
Median Absolute Deviation (MAD)0.091955584
Skewness-0.030869179
Sum2770.2585
Variance0.019117923
MonotonicityNot monotonic
2024-03-07T23:26:51.220694image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.4157735761 1
 
< 0.1%
0.5545951337 1
 
< 0.1%
0.5803654828 1
 
< 0.1%
0.6813921396 1
 
< 0.1%
0.5547833326 1
 
< 0.1%
0.4954541523 1
 
< 0.1%
0.3329628246 1
 
< 0.1%
0.5652775097 1
 
< 0.1%
0.2997003504 1
 
< 0.1%
0.3508994319 1
 
< 0.1%
Other values (5348) 5348
99.8%
ValueCountFrequency (%)
0 1
< 0.1%
0.05376063323 1
< 0.1%
0.07430672799 1
< 0.1%
0.08950969336 1
< 0.1%
0.09527340946 1
< 0.1%
0.09976323485 1
< 0.1%
0.1001849797 1
< 0.1%
0.10785847 1
< 0.1%
0.1108569005 1
< 0.1%
0.1124764764 1
< 0.1%
ValueCountFrequency (%)
1 1
< 0.1%
0.9797258517 1
< 0.1%
0.9525800705 1
< 0.1%
0.9463239261 1
< 0.1%
0.9397973314 1
< 0.1%
0.9266304652 1
< 0.1%
0.9202036788 1
< 0.1%
0.9194938413 1
< 0.1%
0.9122792317 1
< 0.1%
0.8956886274 1
< 0.1%

gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size367.2 KiB
Female
3135 
Male
2223 

Length

Max length6
Median length6
Mean length5.1702128
Min length4

Characters and Unicode

Total characters27702
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMale
2nd rowMale
3rd rowMale
4th rowFemale
5th rowFemale

Common Values

ValueCountFrequency (%)
Female 3135
58.5%
Male 2223
41.5%

Length

2024-03-07T23:26:51.300612image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-07T23:26:51.352830image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
female 3135
58.5%
male 2223
41.5%

Most occurring characters

ValueCountFrequency (%)
e 8493
30.7%
a 5358
19.3%
l 5358
19.3%
F 3135
 
11.3%
m 3135
 
11.3%
M 2223
 
8.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 22344
80.7%
Uppercase Letter 5358
 
19.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 8493
38.0%
a 5358
24.0%
l 5358
24.0%
m 3135
 
14.0%
Uppercase Letter
ValueCountFrequency (%)
F 3135
58.5%
M 2223
41.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 27702
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 8493
30.7%
a 5358
19.3%
l 5358
19.3%
F 3135
 
11.3%
m 3135
 
11.3%
M 2223
 
8.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 27702
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 8493
30.7%
a 5358
19.3%
l 5358
19.3%
F 3135
 
11.3%
m 3135
 
11.3%
M 2223
 
8.0%

heart_disease
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size345.3 KiB
0
5035 
1
 
323

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5358
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 5035
94.0%
1 323
 
6.0%

Length

2024-03-07T23:26:51.403152image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-07T23:26:51.448547image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0 5035
94.0%
1 323
 
6.0%

Most occurring characters

ValueCountFrequency (%)
0 5035
94.0%
1 323
 
6.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5358
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 5035
94.0%
1 323
 
6.0%

Most occurring scripts

ValueCountFrequency (%)
Common 5358
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 5035
94.0%
1 323
 
6.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5358
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 5035
94.0%
1 323
 
6.0%

hypertension
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size345.3 KiB
0
4794 
1
564 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5358
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 4794
89.5%
1 564
 
10.5%

Length

2024-03-07T23:26:51.497915image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-07T23:26:51.543980image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0 4794
89.5%
1 564
 
10.5%

Most occurring characters

ValueCountFrequency (%)
0 4794
89.5%
1 564
 
10.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5358
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4794
89.5%
1 564
 
10.5%

Most occurring scripts

ValueCountFrequency (%)
Common 5358
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4794
89.5%
1 564
 
10.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5358
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4794
89.5%
1 564
 
10.5%

residence_type
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size366.3 KiB
Urban
2731 
Rural
2627 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters26790
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUrban
2nd rowRural
3rd rowRural
4th rowUrban
5th rowUrban

Common Values

ValueCountFrequency (%)
Urban 2731
51.0%
Rural 2627
49.0%

Length

2024-03-07T23:26:51.593990image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-07T23:26:51.641193image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
urban 2731
51.0%
rural 2627
49.0%

Most occurring characters

ValueCountFrequency (%)
r 5358
20.0%
a 5358
20.0%
U 2731
10.2%
b 2731
10.2%
n 2731
10.2%
R 2627
9.8%
u 2627
9.8%
l 2627
9.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 21432
80.0%
Uppercase Letter 5358
 
20.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 5358
25.0%
a 5358
25.0%
b 2731
12.7%
n 2731
12.7%
u 2627
12.3%
l 2627
12.3%
Uppercase Letter
ValueCountFrequency (%)
U 2731
51.0%
R 2627
49.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 26790
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 5358
20.0%
a 5358
20.0%
U 2731
10.2%
b 2731
10.2%
n 2731
10.2%
R 2627
9.8%
u 2627
9.8%
l 2627
9.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 26790
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 5358
20.0%
a 5358
20.0%
U 2731
10.2%
b 2731
10.2%
n 2731
10.2%
R 2627
9.8%
u 2627
9.8%
l 2627
9.8%

smoking_status
Categorical

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size393.1 KiB
never smoked
1982 
Unknown
1591 
formerly smoked
954 
smokes
831 

Length

Max length15
Median length12
Mean length10.118888
Min length6

Characters and Unicode

Total characters54217
Distinct characters15
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowformerly smoked
2nd rownever smoked
3rd rowUnknown
4th rownever smoked
5th rowsmokes

Common Values

ValueCountFrequency (%)
never smoked 1982
37.0%
Unknown 1591
29.7%
formerly smoked 954
17.8%
smokes 831
15.5%

Length

2024-03-07T23:26:51.691853image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-07T23:26:51.741852image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
smoked 2936
35.4%
never 1982
23.9%
unknown 1591
19.2%
formerly 954
 
11.5%
smokes 831
 
10.0%

Most occurring characters

ValueCountFrequency (%)
e 8685
16.0%
n 6755
12.5%
o 6312
11.6%
k 5358
9.9%
m 4721
8.7%
s 4598
8.5%
r 3890
7.2%
2936
 
5.4%
d 2936
 
5.4%
v 1982
 
3.7%
Other values (5) 6044
11.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 49690
91.7%
Space Separator 2936
 
5.4%
Uppercase Letter 1591
 
2.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 8685
17.5%
n 6755
13.6%
o 6312
12.7%
k 5358
10.8%
m 4721
9.5%
s 4598
9.3%
r 3890
7.8%
d 2936
 
5.9%
v 1982
 
4.0%
w 1591
 
3.2%
Other values (3) 2862
 
5.8%
Space Separator
ValueCountFrequency (%)
2936
100.0%
Uppercase Letter
ValueCountFrequency (%)
U 1591
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 51281
94.6%
Common 2936
 
5.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 8685
16.9%
n 6755
13.2%
o 6312
12.3%
k 5358
10.4%
m 4721
9.2%
s 4598
9.0%
r 3890
7.6%
d 2936
 
5.7%
v 1982
 
3.9%
U 1591
 
3.1%
Other values (4) 4453
8.7%
Common
ValueCountFrequency (%)
2936
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 54217
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 8685
16.0%
n 6755
12.5%
o 6312
11.6%
k 5358
9.9%
m 4721
8.7%
s 4598
8.5%
r 3890
7.2%
2936
 
5.4%
d 2936
 
5.4%
v 1982
 
3.7%
Other values (5) 6044
11.1%

stroke
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size345.3 KiB
0
4860 
1
498 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5358
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4860
90.7%
1 498
 
9.3%

Length

2024-03-07T23:26:51.798205image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-07T23:26:51.844514image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0 4860
90.7%
1 498
 
9.3%

Most occurring characters

ValueCountFrequency (%)
0 4860
90.7%
1 498
 
9.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5358
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4860
90.7%
1 498
 
9.3%

Most occurring scripts

ValueCountFrequency (%)
Common 5358
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4860
90.7%
1 498
 
9.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5358
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4860
90.7%
1 498
 
9.3%

work_type
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size383.4 KiB
Private
3073 
Self-employed
884 
Govt_job
690 
children
689 
Never_worked
 
22

Length

Max length13
Median length7
Mean length8.2678238
Min length7

Characters and Unicode

Total characters44299
Distinct characters26
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPrivate
2nd rowPrivate
3rd rowPrivate
4th rowSelf-employed
5th rowPrivate

Common Values

ValueCountFrequency (%)
Private 3073
57.4%
Self-employed 884
 
16.5%
Govt_job 690
 
12.9%
children 689
 
12.9%
Never_worked 22
 
0.4%

Length

2024-03-07T23:26:51.897333image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-07T23:26:51.951017image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
private 3073
57.4%
self-employed 884
 
16.5%
govt_job 690
 
12.9%
children 689
 
12.9%
never_worked 22
 
0.4%

Most occurring characters

ValueCountFrequency (%)
e 6480
14.6%
r 3806
 
8.6%
v 3785
 
8.5%
t 3763
 
8.5%
i 3762
 
8.5%
P 3073
 
6.9%
a 3073
 
6.9%
l 2457
 
5.5%
o 2286
 
5.2%
d 1595
 
3.6%
Other values (16) 10219
23.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 38034
85.9%
Uppercase Letter 4669
 
10.5%
Dash Punctuation 884
 
2.0%
Connector Punctuation 712
 
1.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 6480
17.0%
r 3806
10.0%
v 3785
10.0%
t 3763
9.9%
i 3762
9.9%
a 3073
8.1%
l 2457
 
6.5%
o 2286
 
6.0%
d 1595
 
4.2%
f 884
 
2.3%
Other values (10) 6143
16.2%
Uppercase Letter
ValueCountFrequency (%)
P 3073
65.8%
S 884
 
18.9%
G 690
 
14.8%
N 22
 
0.5%
Dash Punctuation
ValueCountFrequency (%)
- 884
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 712
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 42703
96.4%
Common 1596
 
3.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 6480
15.2%
r 3806
8.9%
v 3785
8.9%
t 3763
8.8%
i 3762
8.8%
P 3073
 
7.2%
a 3073
 
7.2%
l 2457
 
5.8%
o 2286
 
5.4%
d 1595
 
3.7%
Other values (14) 8623
20.2%
Common
ValueCountFrequency (%)
- 884
55.4%
_ 712
44.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 44299
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 6480
14.6%
r 3806
 
8.6%
v 3785
 
8.5%
t 3763
 
8.5%
i 3762
 
8.5%
P 3073
 
6.9%
a 3073
 
6.9%
l 2457
 
5.5%
o 2286
 
5.2%
d 1595
 
3.6%
Other values (16) 10219
23.1%

Interactions

2024-03-07T23:26:47.872534image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-07T23:26:38.070173image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-07T23:26:39.089892image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-07T23:26:39.849567image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-07T23:26:40.603329image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-07T23:26:41.383748image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-07T23:26:42.166634image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-07T23:26:43.168762image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-07T23:26:43.933288image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-07T23:26:44.730160image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-07T23:26:45.517655image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-07T23:26:46.295987image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-07T23:26:47.069151image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-07T23:26:47.927011image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-07T23:26:38.126769image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-07T23:26:39.144237image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-07T23:26:39.903833image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-07T23:26:40.657515image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-07T23:26:41.439724image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-07T23:26:42.223602image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-07T23:26:43.225359image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-07T23:26:43.989737image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-07T23:26:44.788086image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-07T23:26:45.572052image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-07T23:26:46.354097image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-07T23:26:47.126984image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-07T23:26:47.975033image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-07T23:26:38.180827image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-07T23:26:39.198809image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-07T23:26:39.956422image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-07T23:26:40.714583image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-07T23:26:41.495641image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-07T23:26:42.282546image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-07T23:26:43.282481image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-07T23:26:44.049969image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-07T23:26:44.841748image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-07T23:26:45.630887image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-07T23:26:46.404784image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-07T23:26:47.187589image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-07T23:26:48.030318image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-07T23:26:38.499846image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-07T23:26:39.253068image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-07T23:26:40.010159image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-07T23:26:40.768868image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-07T23:26:41.552236image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-07T23:26:42.338957image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-07T23:26:43.339533image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-07T23:26:44.107732image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-07T23:26:44.899406image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-07T23:26:45.687991image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-07T23:26:46.460650image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-07T23:26:47.245427image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-07T23:26:48.338180image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-07T23:26:38.553484image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-07T23:26:39.311659image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-07T23:26:40.065406image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-07T23:26:40.826158image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-07T23:26:41.610731image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-07T23:26:42.397260image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-07T23:26:43.398832image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-07T23:26:44.166305image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-07T23:26:44.959197image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-07T23:26:45.746979image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-07T23:26:46.517932image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-07T23:26:47.303274image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-07T23:26:48.395730image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-07T23:26:38.611080image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-07T23:26:39.368884image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-07T23:26:40.121521image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-07T23:26:40.888345image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-07T23:26:41.668733image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-07T23:26:42.456820image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-07T23:26:43.458623image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-07T23:26:44.222185image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-07T23:26:45.019773image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-07T23:26:45.806385image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-07T23:26:46.576878image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-07T23:26:47.363529image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-07T23:26:48.456717image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-07T23:26:38.670920image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-07T23:26:39.430426image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-07T23:26:40.179783image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-07T23:26:40.950134image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-07T23:26:41.731427image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-07T23:26:42.516846image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-07T23:26:43.520839image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-07T23:26:44.285539image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-07T23:26:45.083601image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-07T23:26:45.870904image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-07T23:26:46.638539image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-07T23:26:47.422847image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-07T23:26:48.519528image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-07T23:26:38.731901image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-07T23:26:39.490431image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-07T23:26:40.242129image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-07T23:26:41.013135image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
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2024-03-07T23:26:42.580977image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-07T23:26:43.579761image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-07T23:26:44.349939image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-07T23:26:45.140105image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-07T23:26:45.933936image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-07T23:26:46.700546image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-07T23:26:47.488860image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-07T23:26:48.571522image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-07T23:26:38.792365image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-07T23:26:39.552361image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-07T23:26:40.304992image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-07T23:26:41.078219image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-07T23:26:41.861200image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-07T23:26:42.646571image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-07T23:26:43.645181image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-07T23:26:44.409279image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
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2024-03-07T23:26:46.765301image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-07T23:26:47.555449image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-07T23:26:48.633510image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-07T23:26:38.853972image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-07T23:26:39.612904image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-07T23:26:40.366112image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
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2024-03-07T23:26:48.692654image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-07T23:26:38.909576image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-07T23:26:39.671659image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
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2024-03-07T23:26:39.033597image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-07T23:26:39.794501image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-07T23:26:40.545003image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-07T23:26:41.327086image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-07T23:26:42.111471image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-07T23:26:43.109883image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-07T23:26:43.880583image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-07T23:26:44.669368image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-07T23:26:45.456613image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-07T23:26:46.242432image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-07T23:26:47.006452image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-07T23:26:47.812612image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Missing values

2024-03-07T23:26:48.914259image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-07T23:26:49.078675image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

ageavg_glucose_levelbmiever_marriedfeat01feat02feat03feat04feat05feat06feat07feat08feat09feat10genderheart_diseasehypertensionresidence_typesmoking_statusstrokework_type
043.000092.710030.5000Yes0.62160.66361.07031.27451.39071.09651.07111.17471.13070.4158Male00Urbanformerly smoked0Private
159.000093.900042.2000Yes0.28580.40511.27220.95031.56041.15081.11030.91000.58480.5955Male00Ruralnever smoked0Private
225.000092.140036.2000Yes0.69590.49631.46681.02630.51391.04080.86991.52530.82230.1276Male00RuralUnknown0Private
374.0000205.840054.6000Yes0.71810.40850.64380.89520.96540.89131.49911.40031.24260.3291Female01Urbannever smoked0Self-employed
434.000079.800037.4000Yes0.47220.46381.16121.30840.80251.57140.31851.51241.31060.3367Female00Urbansmokes0Private
566.000094.390029.4000Yes0.56830.81610.79981.00501.79670.91040.69071.35380.83110.6047Female00UrbanUnknown0Self-employed
622.0000130.340022.0000No0.51940.73080.50781.08831.44520.51420.56011.16940.73240.8311Female00Urbannever smoked0Private
738.000091.000033.3000Yes0.70140.62761.29910.78491.00401.04111.42381.44780.70400.4213Female01Urbannever smoked0Self-employed
869.000083.550028.3000Yes0.76740.45180.58631.15981.15421.35611.63391.03391.45370.3591Female00Urbanformerly smoked0Self-employed
952.000069.110035.2000Yes0.40920.48640.73511.02461.10641.53121.20040.61821.34810.5376Female00Ruralnever smoked0Govt_job
ageavg_glucose_levelbmiever_marriedfeat01feat02feat03feat04feat05feat06feat07feat08feat09feat10genderheart_diseasehypertensionresidence_typesmoking_statusstrokework_type
534972.0000131.410028.4000Yes0.49660.38560.60370.93790.67540.80010.37770.43180.85860.6931Female00Urbannever smoked1Govt_job
535016.0000135.820035.1000No0.42400.52540.71921.40511.22131.31181.52290.82641.43310.6730Male00Urbannever smoked0children
535137.0000176.420039.7000Yes0.45850.38731.32471.25351.36690.89791.33240.65051.49350.5878Male00UrbanUnknown0Private
535253.0000113.740031.6000Yes0.73910.41111.58470.95781.14591.22211.34891.29011.17880.4702Female00Urbansmokes0Self-employed
535325.0000119.960027.7000Yes0.51290.55181.21431.36551.33470.77000.82441.47071.18490.5868Male00Ruralnever smoked0Private
535462.000098.050027.9000Yes0.65880.46311.19280.83521.04320.61921.03601.29210.77890.5882Female00Ruralnever smoked0Private
535578.0000244.9700NaNYes0.24960.41800.84470.71131.80570.97541.42410.70720.87290.4280Male00Urbanformerly smoked1Private
535656.0000227.040023.0000Yes0.59550.60091.25930.24091.08761.04051.35551.35121.02320.7545Female00Ruralsmokes0Private
535777.0000190.690031.4000Yes0.34840.25410.93780.66461.44180.94391.13361.07621.11840.5017Female00Ruralnever smoked1Govt_job
535840.000065.470024.1000Yes0.60880.52980.83140.81401.23630.94050.42191.14131.04340.8551Female00Ruralsmokes0Private